Book Review of: Schecter, I.J. (2019). Do You Ever Cry, Dad? A Father's Guide to Surviving Family Breakup
Bibliographic record
Abstract
In his book "Do You Ever Cry, Dad?" I.J.Schecter delivers a heartbreakingly authentic account of family breakup from a father's perspective.Schecter recalls the emotional distress he faced following his separation with brutal honesty, wearing his heart on his sleeve for the benefit of other dads.With the promise that time heals all wounds, Schecter offers a guidebook for other fathers on how to navigate the choppy and unpredictable waters of divorce.In addition to his own personal experience, he also provides anecdotes from other divorced dads along with insightful input from experts in the field of family studies.Throughout the majority of the book, Schecter emphasizes the connection a father has with his children and the necessity that this connection be maintained via effective parenting styles.An unexpected find was the indirect address of stereotypes that men face during divorces.Intentional or not, Schecter's book certainly goes beyond the limits of a father's self-help book.He teaches fathers how to cope through all the turmoils of divorce while still supporting their children.Schecter wants nothing more out of life than to show his kids unconditional love, which he makes abundantly clear in this book.He serves his role as a father with intense dedication, putting his kids first in every way.At the beginning of the book, Schecter discusses the first few months following his separation and the newfound difficulties that he had to wade through.Dropping his children off at their mother's home was a knife to the heart for Schecter:
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.193 | 0.158 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".